The New Geography of Inequality: Is it all about the Middle or all about the Tails?

By Alex Cobham

How are global and national income distributions evolving over time? And where in these distributions should research into inequality focus? How, in fact should we measure inequality? And do we have sufficient data to do so with reasonable confidence?

On Wednesday 25th June a packed session of the 14th general conference of the European Association of Development Institutes took part in a lively discussion of the nature, measurement and future research agenda on economic inequality.

The research examines the global population peaks that Quah (1996) observes in 1985 data; and shows that the related consumption peaks create a ‘dumbbell’ shape. The major population peak around the dollar-a-day poverty level is associated with a small consumption peak, while the smaller ‘middle class’ population peak between $10 and $50 a day is associated with a much larger consumption peak.

Over time, however, and confirmed in the new PPP data, the second peaks flatten out to leave a trapezoid shape instead – with little consumption peak for the dollar-a-day population peak, and less of a population peak associated with the very pronounced middle class consumption peak. About half of high-income countries’ consumption occurs above the $50-a-day line, although 87% of their populations live below the same line.

Removing upper-middle income countries from the analysis reveals the dumbbell clearly once again, showing that these are the countries where the major change has occurred. Globally, the analysis shows that the gains in terms of the $10 trillion of consumption growth over 1990-2010 have gone disproportionately to higher-income groups. At the extremes, the third of the world population living under $2 a day captured just 5% of the growth in consumption; while the top 1%, earning over $75 a day, captured 15% of the consumption growth.

Finally, Edward and Sumner show that these major changes in the global income distribution are not captured at all by the Gini coefficient of inequality which is largely static across the whole period.

The Palma is an income concentration measure, the ratio of national income shares of the top 10% to the bottom 40%. It takes its name from Gabriel Palma, who first identified the stability of the middle 50% (deciles 5-9) in countries at quite different income levels:

“Basically, it seems that a schoolteacher, a junior or mid-level civil servant, a young professional (other than economics graduates working in financial markets), a skilled worker, middle-manager or a taxi driver who owns his or her own car, all tend to earn the same income across the world — as long as their incomes are normalized by income per capita of respective country.”

Cobham and Sumner show that the stability holds over time, and also across the stages of taxes and transfers where data are available, so that inequality is indeed in the tails. On this basis, the well-known oversensitivity of the Gini coefficient to the middle is a particular problem for an inequality measure. In addition, the decreasing sensitivity of the Gini as inequality rises makes it relatively unsuitable for situations where policymakers may be most concerned.

The lowest observed Palma values are around 1 (i.e. the top 10% has the same income share as the bottom 40%, so that each person in the top 10% has around four times the income of each person in the bottom 40%). Equivalent Gini values are 0.25-0.30. At high inequality, the Palma takes values of around 7 (e.g. South Africa in 2008, with a Gini of 0.63) up to as much as 15 (Jamaica in 2002, with a Gini of 0.66).

Finally, in response to criticisms that the Palma ignores too much information by focusing only on the tails, the authors show that the leading Gini series (those published by the World Bank and by UNU-WIDER) can be almost perfectly predicted on the basis of the two components of the Palma alone: the top 10% and bottom 40% income shares. If y and x are the income shares of the tails, then on the basis of the regression result for the World Bank (PovCal) Gini series, the choice between Palma and Gini is this:

P = y/x

G = 0.581y – 1.195x + 0.419

With the possibility of policy targets in mind, the advantages of the Palma seem clear – perhaps explaining why there are a number of proposals for post-2015 targets based on the Palma already, including that of Michael Doyle and Joseph Stiglitz for countries to achieve a Palma of 1 by 2030.

Data issues, income categorisation and further research

Nancy Birdsall, president of the Center for Global Development continued the discussion, reflecting on the two presentations and introducing some results from her paper ‘The Strugglers: The New Poor in Latin America?’. Prof Birdsall highlighted reasons to consider the $10-a-day level as socially meaningful, since World Bank research in Latin America has shown a diminishing likelihood of falling back into poverty as daily income rises from $5 to $10; and from an Argentinian survey in which, broadly speaking, the daily income level of $10 was associated with self-identification as ‘middle class’. Prof Birdsall also cited her research on the median as a ‘distribution-aware’ measure of income, noting that median income in the developing world is around $3 a day.

Stefano Prato (Society for International Development, Nairobi) chaired a lively discussion afterwards in which a number of key points emerged. One was that none the work presented involved adjustments for the known problems of undeclared income (and wealth) at the top end of global and national distributions – and that doing so is likely to change the picture in some cases dramatically. Prof Birdsall highlighted the importance of a global wealth register, or equivalent, to pursue Piketty-type direct taxation proposals.

Another point raised was the need for caution when using group categories (e.g. the bottom 40%, or those earning $4-$10 a day), without necessarily having a clear sociological basis. Categorisation can aid discussion of quantitative findings, but risks reification which may not be justified.

Other questions dealt with the potential for further research into the health and wider human development impacts of inequality in lower-income countries; the relative, and apparently crisi-resistant growth in consumption of pets in high-income countries; and the extent to which Palma measures published in the State of East Africa Report 2013 had immediately cut through to policymakers (sometimes uncomfortably so).